Overview

Dataset statistics

Number of variables26
Number of observations1276444
Missing cells34
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory253.2 MiB
Average record size in memory208.0 B

Variable types

NUM12
CAT12
BOOL2

Reproduction

Analysis started2021-09-12 06:15:43.504233
Analysis finished2021-09-12 06:19:01.245819
Duration3 minutes and 17.74 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

GRID_TYPE has constant value "Shot Chart Detail" Constant
SHOT_ATTEMPTED_FLAG has constant value "1" Constant
PLAYER_NAME has a high cardinality: 539 distinct values High cardinality
ACTION_TYPE has a high cardinality: 65 distinct values High cardinality
PERIOD is highly correlated with GAME_EVENT_IDHigh correlation
GAME_EVENT_ID is highly correlated with PERIODHigh correlation
GAME_DATE is highly correlated with GAME_IDHigh correlation
GAME_ID is highly correlated with GAME_DATEHigh correlation
SHOT_ZONE_BASIC is highly correlated with SHOT_TYPEHigh correlation
SHOT_TYPE is highly correlated with SHOT_ZONE_BASIC and 1 other fieldsHigh correlation
SHOT_ZONE_RANGE is highly correlated with SHOT_TYPEHigh correlation
MINUTES_REMAINING has 125753 (9.9%) zeros Zeros
SECONDS_REMAINING has 34969 (2.7%) zeros Zeros
SHOT_DISTANCE has 121519 (9.5%) zeros Zeros
LOC_X has 63747 (5.0%) zeros Zeros
LOC_Y has 28339 (2.2%) zeros Zeros

Variables

df_index
Real number (ℝ≥0)

Distinct count190983
Unique (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74038.43202130293
Minimum0
Maximum190982
Zeros10
Zeros (%)< 0.1%
Memory size9.7 MiB
2021-09-12T03:19:01.387952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6382
Q131911
median66339
Q3111750.25
95-th percentile165242.85
Maximum190982
Range190982
Interquartile range (IQR)79839.25

Descriptive statistics

Standard deviation49713.34802
Coefficient of variation (CV)0.6714532799
Kurtosis-0.8410931585
Mean74038.43202
Median Absolute Deviation (MAD)38547
Skewness0.4371240023
Sum9.450591232e+10
Variance2471416972
2021-09-12T03:19:01.503629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
204710< 0.1%
 
260510< 0.1%
 
2205410< 0.1%
 
2000510< 0.1%
 
1795610< 0.1%
 
3229110< 0.1%
 
3024210< 0.1%
 
2819310< 0.1%
 
2614410< 0.1%
 
3948710< 0.1%
 
Other values (190973)1276344> 99.9%
 
ValueCountFrequency (%) 
010< 0.1%
 
110< 0.1%
 
210< 0.1%
 
310< 0.1%
 
410< 0.1%
 
ValueCountFrequency (%) 
1909821< 0.1%
 
1909811< 0.1%
 
1909801< 0.1%
 
1909791< 0.1%
 
1909781< 0.1%
 

GRID_TYPE
Categorical

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
Shot Chart Detail
1276444
ValueCountFrequency (%) 
Shot Chart Detail1276444100.0%
 
2021-09-12T03:19:01.649305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

GAME_ID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count11716
Unique (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21663631.58017743
Minimum21100001
Maximum22001080
Zeros0
Zeros (%)0.0%
Memory size9.7 MiB
2021-09-12T03:19:01.764757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum21100001
5-th percentile21200433
Q121500321
median21700633
Q321900306
95-th percentile22000720
Maximum22001080
Range901079
Interquartile range (IQR)399985

Descriptive statistics

Standard deviation256973.2623
Coefficient of variation (CV)0.01186196605
Kurtosis-0.7919934066
Mean21663631.58
Median Absolute Deviation (MAD)199911
Skewness-0.4643588932
Sum2.765241255e+13
Variance6.603525753e+10
2021-09-12T03:19:01.862024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
21800639216< 0.1%
 
22000216215< 0.1%
 
22000811214< 0.1%
 
21800928214< 0.1%
 
22001012214< 0.1%
 
21900787213< 0.1%
 
21800881213< 0.1%
 
21901281213< 0.1%
 
21900818213< 0.1%
 
21800920212< 0.1%
 
Other values (11706)127430799.8%
 
ValueCountFrequency (%) 
2110000151< 0.1%
 
2110000225< 0.1%
 
2110000317< 0.1%
 
2110000463< 0.1%
 
2110000548< 0.1%
 
ValueCountFrequency (%) 
22001080185< 0.1%
 
22001079192< 0.1%
 
22001078202< 0.1%
 
22001077176< 0.1%
 
22001076185< 0.1%
 

GAME_EVENT_ID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count898
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.2626719229359
Minimum1
Maximum1012
Zeros0
Zeros (%)0.0%
Memory size9.7 MiB
2021-09-12T03:19:01.985731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile23
Q1130
median285
Q3435
95-th percentile603
Maximum1012
Range1011
Interquartile range (IQR)305

Descriptive statistics

Standard deviation184.0131
Coefficient of variation (CV)0.6317771472
Kurtosis-0.9477964547
Mean291.2626719
Median Absolute Deviation (MAD)152
Skewness0.2108098555
Sum371780490
Variance33860.82096
2021-09-12T03:19:02.107528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
750620.4%
 
942060.3%
 
1136220.3%
 
1334860.3%
 
1533810.3%
 
1732920.3%
 
1932850.3%
 
1632450.3%
 
2132360.3%
 
2732260.3%
 
Other values (888)124040397.2%
 
ValueCountFrequency (%) 
14< 0.1%
 
227540.2%
 
315510.1%
 
419800.2%
 
517120.1%
 
ValueCountFrequency (%) 
10121< 0.1%
 
9861< 0.1%
 
9841< 0.1%
 
9801< 0.1%
 
9791< 0.1%
 

PLAYER_ID
Real number (ℝ≥0)

Distinct count539
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean581660.0054745841
Minimum2544
Maximum1630466
Zeros0
Zeros (%)0.0%
Memory size9.7 MiB
2021-09-12T03:19:02.253698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2544
5-th percentile101150
Q1201949
median203087
Q31626161
95-th percentile1629028
Maximum1630466
Range1627922
Interquartile range (IQR)1424212

Descriptive statistics

Standard deviation640338.8094
Coefficient of variation (CV)1.100881621
Kurtosis-0.9513247179
Mean581660.0055
Median Absolute Deviation (MAD)1150
Skewness1.01365147
Sum7.42456424e+11
Variance4.100337909e+11
2021-09-12T03:19:02.396316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
201566141261.1%
 
201935133951.0%
 
2544127141.0%
 
203081125441.0%
 
201942121471.0%
 
202689115270.9%
 
201939110970.9%
 
200746109480.9%
 
202331107040.8%
 
203078105830.8%
 
Other values (529)115665990.6%
 
ValueCountFrequency (%) 
2544127141.0%
 
2546105070.8%
 
261711380.1%
 
273055350.4%
 
273842060.3%
 
ValueCountFrequency (%) 
163046667< 0.1%
 
163027384< 0.1%
 
1630271108< 0.1%
 
16302687< 0.1%
 
1630267315< 0.1%
 

PLAYER_NAME
Categorical

HIGH CARDINALITY

Distinct count539
Unique (%)< 0.1%
Missing34
Missing (%)< 0.1%
Memory size9.7 MiB
Russell Westbrook
 
14126
James Harden
 
13395
LeBron James
 
12714
Damian Lillard
 
12544
DeMar DeRozan
 
12147
Other values (534)
1211484
ValueCountFrequency (%) 
Russell Westbrook141261.1%
 
James Harden133951.0%
 
LeBron James127141.0%
 
Damian Lillard125441.0%
 
DeMar DeRozan121471.0%
 
Kemba Walker115270.9%
 
Stephen Curry110970.9%
 
LaMarcus Aldridge109480.9%
 
Paul George107040.8%
 
Bradley Beal105830.8%
 
Other values (529)115662590.6%
 
2021-09-12T03:19:02.558886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length24
Median length13
Mean length13.08703633
Min length3

TEAM_ID
Real number (ℝ≥0)

Distinct count30
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1610612751.783518
Minimum1610612737
Maximum1610612766
Zeros0
Zeros (%)0.0%
Memory size9.7 MiB
2021-09-12T03:19:02.674813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1610612737
5-th percentile1610612738
Q11610612744
median1610612752
Q31610612760
95-th percentile1610612765
Maximum1610612766
Range29
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.697129776
Coefficient of variation (CV)5.399888811e-09
Kurtosis-1.218485629
Mean1610612752
Median Absolute Deviation (MAD)8
Skewness-0.09213667102
Sum2.055856983e+15
Variance75.64006635
2021-09-12T03:19:02.779532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1610612760550214.3%
 
1610612757541894.2%
 
1610612761527904.1%
 
1610612762502673.9%
 
1610612753468813.7%
 
1610612738459913.6%
 
1610612746452593.5%
 
1610612737452533.5%
 
1610612756452423.5%
 
1610612755451403.5%
 
Other values (20)79041161.9%
 
ValueCountFrequency (%) 
1610612737452533.5%
 
1610612738459913.6%
 
1610612739431443.4%
 
1610612740430113.4%
 
1610612741414633.2%
 
ValueCountFrequency (%) 
1610612766376813.0%
 
1610612765430193.4%
 
1610612764440353.4%
 
1610612763368012.9%
 
1610612762502673.9%
 

TEAM_NAME
Categorical

Distinct count34
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
Oklahoma City Thunder
 
55021
Portland Trail Blazers
 
54189
Toronto Raptors
 
52790
Utah Jazz
 
50267
Orlando Magic
 
46881
Other values (29)
1017296
ValueCountFrequency (%) 
Oklahoma City Thunder550214.3%
 
Portland Trail Blazers541894.2%
 
Toronto Raptors527904.1%
 
Utah Jazz502673.9%
 
Orlando Magic468813.7%
 
Boston Celtics459913.6%
 
Atlanta Hawks452533.5%
 
Phoenix Suns452423.5%
 
Philadelphia 76ers451403.5%
 
Minnesota Timberwolves441643.5%
 
Other values (24)79150662.0%
 
2021-09-12T03:19:03.421102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length22
Median length15
Mean length15.90998273
Min length9

PERIOD
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count8
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.466396488995992
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size9.7 MiB
2021-09-12T03:19:03.540976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.136434753
Coefficient of variation (CV)0.4607672603
Kurtosis-1.194522726
Mean2.466396489
Median Absolute Deviation (MAD)1
Skewness0.1040547853
Sum3148217
Variance1.291483948
2021-09-12T03:19:03.648493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
134105026.7%
 
332378125.4%
 
231071524.3%
 
429163322.8%
 
580080.6%
 
610260.1%
 
7182< 0.1%
 
849< 0.1%
 
ValueCountFrequency (%) 
134105026.7%
 
231071524.3%
 
332378125.4%
 
429163322.8%
 
580080.6%
 
ValueCountFrequency (%) 
849< 0.1%
 
7182< 0.1%
 
610260.1%
 
580080.6%
 
429163322.8%
 

MINUTES_REMAINING
Real number (ℝ≥0)

ZEROS

Distinct count13
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.322060348906807
Minimum0
Maximum12
Zeros125753
Zeros (%)9.9%
Memory size9.7 MiB
2021-09-12T03:19:03.775186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum12
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.42832508
Coefficient of variation (CV)0.6441725301
Kurtosis-1.201199466
Mean5.322060349
Median Absolute Deviation (MAD)3
Skewness0.02421514856
Sum6793312
Variance11.75341286
2021-09-12T03:19:03.877426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01257539.9%
 
41095608.6%
 
51082468.5%
 
61081508.5%
 
31079348.5%
 
71073348.4%
 
81063678.3%
 
91055998.3%
 
101042688.2%
 
21042548.2%
 
Other values (3)18897914.8%
 
ValueCountFrequency (%) 
01257539.9%
 
11041508.2%
 
21042548.2%
 
31079348.5%
 
41095608.6%
 
ValueCountFrequency (%) 
1218< 0.1%
 
11848116.6%
 
101042688.2%
 
91055998.3%
 
81063678.3%
 

SECONDS_REMAINING
Real number (ℝ≥0)

ZEROS

Distinct count60
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.82820554603257
Minimum0
Maximum59
Zeros34969
Zeros (%)2.7%
Memory size9.7 MiB
2021-09-12T03:19:03.998614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q114
median29
Q344
95-th percentile56
Maximum59
Range59
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.43893875
Coefficient of variation (CV)0.6049262666
Kurtosis-1.194264053
Mean28.82820555
Median Absolute Deviation (MAD)15
Skewness0.006981554965
Sum36797590
Variance304.1165848
2021-09-12T03:19:04.112861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0349692.7%
 
1255782.0%
 
2230711.8%
 
3220261.7%
 
45219451.7%
 
43219071.7%
 
41218401.7%
 
4217781.7%
 
29217171.7%
 
46216961.7%
 
Other values (50)103991781.5%
 
ValueCountFrequency (%) 
0349692.7%
 
1255782.0%
 
2230711.8%
 
3220261.7%
 
4217781.7%
 
ValueCountFrequency (%) 
59197851.6%
 
58196331.5%
 
57193141.5%
 
56198451.6%
 
55193211.5%
 

EVENT_TYPE
Categorical

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
Missed Shot
686416
Made Shot
590028
ValueCountFrequency (%) 
Missed Shot68641653.8%
 
Made Shot59002846.2%
 
2021-09-12T03:19:04.284640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.07551291
Min length9

ACTION_TYPE
Categorical

HIGH CARDINALITY

Distinct count65
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
Jump Shot
521272
Pullup Jump shot
108070
Driving Layup Shot
 
103735
Layup Shot
 
93012
Step Back Jump shot
 
38338
Other values (60)
412017
ValueCountFrequency (%) 
Jump Shot52127240.8%
 
Pullup Jump shot1080708.5%
 
Driving Layup Shot1037358.1%
 
Layup Shot930127.3%
 
Step Back Jump shot383383.0%
 
Driving Floating Jump Shot301632.4%
 
Floating Jump shot276572.2%
 
Running Layup Shot225351.8%
 
Cutting Layup Shot214641.7%
 
Tip Layup Shot204311.6%
 
Other values (55)28976722.7%
 
2021-09-12T03:19:04.427658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length34
Median length10
Mean length13.93270602
Min length7

SHOT_TYPE
Categorical

HIGH CORRELATION

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
2PT Field Goal
864702
3PT Field Goal
411742
ValueCountFrequency (%) 
2PT Field Goal86470267.7%
 
3PT Field Goal41174232.3%
 
2021-09-12T03:19:04.554721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

SHOT_ZONE_BASIC
Categorical

HIGH CORRELATION

Distinct count7
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
Restricted Area
416869
Above the Break 3
314254
Mid-Range
247304
In The Paint (Non-RA)
200800
Left Corner 3
 
48782
Other values (2)
 
48435
ValueCountFrequency (%) 
Restricted Area41686932.7%
 
Above the Break 331425424.6%
 
Mid-Range24730419.4%
 
In The Paint (Non-RA)20080015.7%
 
Left Corner 3487823.8%
 
Right Corner 3458473.6%
 
Backcourt25880.2%
 
2021-09-12T03:19:04.694393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length21
Median length15
Mean length15.14927799
Min length9

SHOT_ZONE_AREA
Categorical

Distinct count6
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
Center(C)
726621
Left Side Center(LC)
155509
Right Side Center(RC)
153015
Left Side(L)
 
122464
Right Side(R)
 
115942
ValueCountFrequency (%) 
Center(C)72662156.9%
 
Left Side Center(LC)15550912.2%
 
Right Side Center(RC)15301512.0%
 
Left Side(L)1224649.6%
 
Right Side(R)1159429.1%
 
Back Court(BC)28930.2%
 
2021-09-12T03:19:04.837535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length21
Median length9
Mean length12.4411255
Min length9

SHOT_ZONE_RANGE
Categorical

HIGH CORRELATION

Distinct count5
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
Less Than 8 ft.
536076
24+ ft.
408578
8-16 ft.
173375
16-24 ft.
155522
Back Court Shot
 
2893
ValueCountFrequency (%) 
Less Than 8 ft.53607642.0%
 
24+ ft.40857832.0%
 
8-16 ft.17337513.6%
 
16-24 ft.15552212.2%
 
Back Court Shot28930.2%
 
2021-09-12T03:19:04.983057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length15
Median length9
Mean length10.75744725
Min length7

SHOT_DISTANCE
Real number (ℝ≥0)

ZEROS

Distinct count88
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.771660174672762
Minimum0
Maximum87
Zeros121519
Zeros (%)9.5%
Memory size9.7 MiB
2021-09-12T03:19:05.098807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median12
Q324
95-th percentile26
Maximum87
Range87
Interquartile range (IQR)22

Descriptive statistics

Standard deviation10.31249031
Coefficient of variation (CV)0.8074510418
Kurtosis-0.8474133278
Mean12.77166017
Median Absolute Deviation (MAD)11
Skewness0.2401173046
Sum16302309
Variance106.3474565
2021-09-12T03:19:05.192003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
114671911.5%
 
251242839.7%
 
01215199.5%
 
21009517.9%
 
24861416.7%
 
26681905.3%
 
23569004.5%
 
3476803.7%
 
22347042.7%
 
4341192.7%
 
Other values (78)45523835.7%
 
ValueCountFrequency (%) 
01215199.5%
 
114671911.5%
 
21009517.9%
 
3476803.7%
 
4341192.7%
 
ValueCountFrequency (%) 
872< 0.1%
 
867< 0.1%
 
854< 0.1%
 
846< 0.1%
 
838< 0.1%
 

LOC_X
Real number (ℝ)

ZEROS

Distinct count501
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.1025889110685623
Minimum-250
Maximum250
Zeros63747
Zeros (%)5.0%
Memory size9.7 MiB
2021-09-12T03:19:05.301893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-250
5-th percentile-208
Q1-47
median0
Q344
95-th percentile203
Maximum250
Range500
Interquartile range (IQR)91

Descriptive statistics

Standard deviation107.9558351
Coefficient of variation (CV)-97.91122879
Kurtosis0.06100707598
Mean-1.102588911
Median Absolute Deviation (MAD)46
Skewness-0.02299650566
Sum-1407393
Variance11654.46234
2021-09-12T03:19:05.412620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0637475.0%
 
2196261.5%
 
-2194941.5%
 
9176121.4%
 
6167031.3%
 
1157031.2%
 
4139151.1%
 
-5134821.1%
 
-4132241.0%
 
-11128831.0%
 
Other values (491)107005583.8%
 
ValueCountFrequency (%) 
-25075< 0.1%
 
-2496< 0.1%
 
-248139< 0.1%
 
-24723< 0.1%
 
-246231< 0.1%
 
ValueCountFrequency (%) 
25045< 0.1%
 
2497< 0.1%
 
24877< 0.1%
 
24750< 0.1%
 
246142< 0.1%
 

LOC_Y
Real number (ℝ)

ZEROS

Distinct count859
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.28210246591311
Minimum-52
Maximum867
Zeros28339
Zeros (%)2.2%
Memory size9.7 MiB
2021-09-12T03:19:05.531263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-52
5-th percentile-3
Q111
median48
Q3169
95-th percentile251
Maximum867
Range919
Interquartile range (IQR)158

Descriptive statistics

Standard deviation92.68699423
Coefficient of variation (CV)1.03813633
Kurtosis0.4448280308
Mean89.28210247
Median Absolute Deviation (MAD)47
Skewness0.8763117573
Sum113963604
Variance8590.878899
2021-09-12T03:19:05.618037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1364272.9%
 
11311902.4%
 
0283392.2%
 
7271302.1%
 
-6220781.7%
 
6212301.7%
 
8201121.6%
 
3195911.5%
 
16195211.5%
 
4192931.5%
 
Other values (849)103153380.8%
 
ValueCountFrequency (%) 
-521< 0.1%
 
-517< 0.1%
 
-497< 0.1%
 
-481< 0.1%
 
-472< 0.1%
 
ValueCountFrequency (%) 
8671< 0.1%
 
8651< 0.1%
 
8611< 0.1%
 
8571< 0.1%
 
8551< 0.1%
 

SHOT_ATTEMPTED_FLAG
Boolean

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
1
1276444
ValueCountFrequency (%) 
11276444100.0%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
0
686416
1
590028
ValueCountFrequency (%) 
068641653.8%
 
159002846.2%
 

GAME_DATE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count1557
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20173330.324318185
Minimum20111225
Maximum20210516
Zeros0
Zeros (%)0.0%
Memory size9.7 MiB
2021-09-12T03:19:05.723759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum20111225
5-th percentile20121228
Q120151209
median20180113
Q320191204
95-th percentile20210402
Maximum20210516
Range99291
Interquartile range (IQR)39995

Descriptive statistics

Standard deviation26251.94615
Coefficient of variation (CV)0.001301319402
Kurtosis-0.8446801193
Mean20173330.32
Median Absolute Deviation (MAD)19989
Skewness-0.3418815783
Sum2.575012645e+13
Variance689164676.6
2021-09-12T03:19:05.819421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2021051627020.2%
 
2020012024390.2%
 
2019122823740.2%
 
2019112723470.2%
 
2019112921820.2%
 
2018112321760.2%
 
2021012721520.2%
 
2021041421470.2%
 
2020122321470.2%
 
2021042121280.2%
 
Other values (1547)125365098.2%
 
ValueCountFrequency (%) 
20111225204< 0.1%
 
20111226507< 0.1%
 
20111227182< 0.1%
 
20111228432< 0.1%
 
20111229208< 0.1%
 
ValueCountFrequency (%) 
2021051627020.2%
 
2021051511040.1%
 
2021051414150.1%
 
2021051315910.1%
 
2021051210830.1%
 

HTM
Categorical

Distinct count32
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
OKC
 
48950
POR
 
48779
TOR
 
46986
UTA
 
45560
ATL
 
44465
Other values (27)
1041704
ValueCountFrequency (%) 
OKC489503.8%
 
POR487793.8%
 
TOR469863.7%
 
UTA455603.6%
 
ATL444653.5%
 
PHX443893.5%
 
ORL443443.5%
 
BOS443443.5%
 
LAC439353.4%
 
PHI438533.4%
 
Other values (22)82083964.3%
 
2021-09-12T03:19:05.972028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

VTM
Categorical

Distinct count32
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
OKC
 
49196
POR
 
49136
TOR
 
47023
UTA
 
46446
ORL
 
44491
Other values (27)
1040152
ValueCountFrequency (%) 
OKC491963.9%
 
POR491363.8%
 
TOR470233.7%
 
UTA464463.6%
 
ORL444913.5%
 
PHI440483.5%
 
BOS438693.4%
 
WAS436493.4%
 
ATL435403.4%
 
LAC434903.4%
 
Other values (22)82155664.4%
 
2021-09-12T03:19:06.105029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

SEASON_ID
Categorical

Distinct count10
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
2020-21
190983
2018-19
188656
2019-20
179913
2017-18
160274
2016-17
145805
Other values (5)
410813
ValueCountFrequency (%) 
2020-2119098315.0%
 
2018-1918865614.8%
 
2019-2017991314.1%
 
2017-1816027412.6%
 
2016-1714580511.4%
 
2015-161239869.7%
 
2014-15969587.6%
 
2013-14823616.5%
 
2012-13659255.2%
 
2011-12415833.3%
 
2021-09-12T03:19:06.244729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Interactions

2021-09-12T03:17:11.871994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:12.556233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:13.193520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:13.811905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:14.443875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:15.086663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:15.689945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:16.347386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:16.996660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:17.654673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:18.370425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:19.045616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:19.678942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:20.297820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:20.919196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:21.514772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:22.147380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:22.749282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:23.354019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:23.950985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:24.554370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:25.203681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:25.811732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:26.457825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:27.107774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:27.748169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:28.398432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:29.220601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:29.901474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:30.548924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:31.169194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:31.767465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:32.423815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:33.057134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:33.664027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:34.261977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:34.854148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:35.578914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:36.286638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:36.983792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:37.719456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:38.402677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:39.160223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:39.908258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:40.577351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:41.236121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:41.854899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:42.479810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:43.154876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:43.814095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:44.427551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:45.012852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:45.662746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:46.244068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:46.870949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:47.506775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:48.209500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:48.817464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:49.500694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:50.139005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:50.763622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:51.356648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:51.967590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:52.646295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:53.295400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:53.887369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:54.467965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:55.042428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:55.675666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:56.305480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:56.890795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:57.535631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:58.177572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:58.840797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:17:59.499044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:00.222247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:00.880652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:01.464576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:02.057587image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:02.660096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:03.272025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:03.922343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:04.518827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:05.128803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:05.714760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:06.350354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:06.951636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:07.554922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:08.175301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:08.811616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:09.392798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:10.043604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:10.725073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:11.465712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:12.120817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:12.798430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:13.455832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:14.265791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:14.935698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:15.558719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:16.165917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:16.734793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:17.300431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:18.136681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:18.750549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:19.421356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:20.065388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:20.708572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:21.505302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:22.137627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:22.826591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:23.427912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:24.091253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:24.713737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:25.347532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:25.967981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:26.807774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:27.487840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:28.157240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:28.806681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:29.466594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:30.083787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:30.745872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:31.439012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:32.088721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:32.654508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:33.254248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:33.887752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:34.501234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:35.142565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:35.776893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:36.366843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:36.961326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:37.626602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:38.288942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:38.956313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:39.620027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:40.280817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:40.919613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:41.769335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:42.446835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:43.086457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:43.730110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:44.335605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-09-12T03:19:06.367292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-12T03:19:06.895980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-12T03:19:07.425199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-12T03:19:07.936810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-12T03:19:08.513834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-12T03:18:47.434129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:52.731179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-12T03:18:59.337312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

df_indexGRID_TYPEGAME_IDGAME_EVENT_IDPLAYER_IDPLAYER_NAMETEAM_IDTEAM_NAMEPERIODMINUTES_REMAININGSECONDS_REMAININGEVENT_TYPEACTION_TYPESHOT_TYPESHOT_ZONE_BASICSHOT_ZONE_AREASHOT_ZONE_RANGESHOT_DISTANCELOC_XLOC_YSHOT_ATTEMPTED_FLAGSHOT_MADE_FLAGGAME_DATEHTMVTMSEASON_ID
00Shot Chart Detail2200001227203932Aaron Gordon1610612753Orlando Magic1956Missed ShotJump Shot3PT Field GoalAbove the Break 3Left Side Center(LC)24+ ft.24-1461991020201223ORLMIA2020-21
11Shot Chart Detail2200001240203932Aaron Gordon1610612753Orlando Magic1855Made ShotRunning Dunk Shot2PT Field GoalRestricted AreaCenter(C)Less Than 8 ft.0-451120201223ORLMIA2020-21
22Shot Chart Detail2200001260203932Aaron Gordon1610612753Orlando Magic1710Missed ShotStep Back Jump shot3PT Field GoalAbove the Break 3Left Side Center(LC)24+ ft.25-1541981020201223ORLMIA2020-21
33Shot Chart Detail2200001264203932Aaron Gordon1610612753Orlando Magic1634Made ShotDunk Shot2PT Field GoalRestricted AreaCenter(C)Less Than 8 ft.0-4-41120201223ORLMIA2020-21
44Shot Chart Detail2200001275203932Aaron Gordon1610612753Orlando Magic1536Made ShotTip Layup Shot2PT Field GoalRestricted AreaCenter(C)Less Than 8 ft.0001120201223ORLMIA2020-21
55Shot Chart Detail22000012183203932Aaron Gordon1610612753Orlando Magic2950Missed ShotStep Back Jump shot3PT Field GoalAbove the Break 3Left Side Center(LC)24+ ft.25-1871711020201223ORLMIA2020-21
66Shot Chart Detail22000012249203932Aaron Gordon1610612753Orlando Magic2551Made ShotLayup Shot2PT Field GoalRestricted AreaCenter(C)Less Than 8 ft.2-4271120201223ORLMIA2020-21
77Shot Chart Detail22000012515203932Aaron Gordon1610612753Orlando Magic41134Made ShotJump Shot3PT Field GoalRight Corner 3Right Side(R)24+ ft.23230411120201223ORLMIA2020-21
88Shot Chart Detail22000012539203932Aaron Gordon1610612753Orlando Magic4941Made ShotJump Shot2PT Field GoalMid-RangeRight Side(R)16-24 ft.19167941120201223ORLMIA2020-21
99Shot Chart Detail22000012557203932Aaron Gordon1610612753Orlando Magic4833Made ShotRunning Alley Oop Dunk Shot2PT Field GoalRestricted AreaCenter(C)Less Than 8 ft.26261120201223ORLMIA2020-21

Last rows

df_indexGRID_TYPEGAME_IDGAME_EVENT_IDPLAYER_IDPLAYER_NAMETEAM_IDTEAM_NAMEPERIODMINUTES_REMAININGSECONDS_REMAININGEVENT_TYPEACTION_TYPESHOT_TYPESHOT_ZONE_BASICSHOT_ZONE_AREASHOT_ZONE_RANGESHOT_DISTANCELOC_XLOC_YSHOT_ATTEMPTED_FLAGSHOT_MADE_FLAGGAME_DATEHTMVTMSEASON_ID
127643441573Shot Chart Detail21100988204202083Wesley Matthews1610612757Portland Trail Blazers2231Made ShotJump Shot3PT Field GoalRight Corner 3Right Side(R)24+ ft.22228231120120426UTAPOR2011-12
127643541574Shot Chart Detail21100988214202083Wesley Matthews1610612757Portland Trail Blazers2122Missed ShotJump Shot3PT Field GoalLeft Corner 3Left Side(L)24+ ft.23-228421020120426UTAPOR2011-12
127643641575Shot Chart Detail21100988230202083Wesley Matthews1610612757Portland Trail Blazers2040Missed ShotJump Shot2PT Field GoalRestricted AreaCenter(C)Less Than 8 ft.329231020120426UTAPOR2011-12
127643741576Shot Chart Detail21100988269202083Wesley Matthews1610612757Portland Trail Blazers3836Missed ShotJump Shot2PT Field GoalMid-RangeRight Side(R)8-16 ft.1414491020120426UTAPOR2011-12
127643841577Shot Chart Detail21100988292202083Wesley Matthews1610612757Portland Trail Blazers3616Missed ShotJump Shot3PT Field GoalAbove the Break 3Right Side Center(RC)24+ ft.231062141020120426UTAPOR2011-12
127643941578Shot Chart Detail21100988319202083Wesley Matthews1610612757Portland Trail Blazers3344Missed ShotJump Shot3PT Field GoalAbove the Break 3Right Side Center(RC)24+ ft.242041351020120426UTAPOR2011-12
127644041579Shot Chart Detail21100988388202083Wesley Matthews1610612757Portland Trail Blazers4918Made ShotJump Shot3PT Field GoalAbove the Break 3Left Side Center(LC)24+ ft.24-1931491120120426UTAPOR2011-12
127644141580Shot Chart Detail21100988435202083Wesley Matthews1610612757Portland Trail Blazers451Missed ShotJump Shot3PT Field GoalLeft Corner 3Left Side(L)24+ ft.23-231201020120426UTAPOR2011-12
127644241581Shot Chart Detail21100988444202083Wesley Matthews1610612757Portland Trail Blazers445Missed ShotJump Shot2PT Field GoalMid-RangeLeft Side Center(LC)16-24 ft.16-861451020120426UTAPOR2011-12
127644341582Shot Chart Detail21100988490202083Wesley Matthews1610612757Portland Trail Blazers402Missed ShotJump Shot3PT Field GoalAbove the Break 3Center(C)24+ ft.24-42441020120426UTAPOR2011-12